scholarly journals A Hybrid Genetic Algorithm for the Quadratic Assignment Problem on Graphics Processing Units

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
ERDENER ÖZÇETİN ◽  
GÜRKAN ÖZTÜRK
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Graphics Processing Units ◽  
Quadratic Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Quadratic Assignment ◽  
Graphics Processing

scholarly journals Combinatorial optimization np-hard problem solved by using the quadratic assignment problem (QAP) solution through a parallel genetic algorithm on GPU

2018 ◽  
Vol 11 (2) ◽  
pp. 146-151
Author(s):  
Eduardo Cárdenas Gómez ◽  
Roberto Poveda Chaves ◽  
Orlando García Hurtado
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Assignment Problem ◽  
Graphics Processing Units ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment ◽  
Np Hard ◽  
Parallel Genetic Algorithm ◽  
Hard Problems ◽  
Graphics Processing

In this article, some instances of well known combinatorial optimization NP-Hard problems are solved by using Koopmans and Beckmann formulation of the quadratic assignment problem (QAP). These instances are solved by using an Embarrassingly Parallel Genetic Algorithm or by using an Island Parallel Genetic Algorithm; in both cases, the implementation is carried out on Graphics Processing Units (GPUs).

scholarly journals Experimental Analysis of Hybrid Genetic Algorithm for the Grey Pattern Quadratic Assignment Problem

2019 ◽  
Vol 48 (2) ◽  
pp. 335-356
Author(s):  
Evelina Stanevičienė ◽  
Alfonsas Misevičius ◽  
Armantas Ostreika
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Quadratic Assignment ◽  
Initial Population ◽  
Local Improvement ◽  
Grey Pattern ◽  
Overall Performance ◽  
Selection Of

In this paper, we present the results of the extensive computational experiments with the hybrid genetic algorithm (HGA) for solving the grey pattern quadratic assignment problem (GP-QAP). The experiments are on the basis of the component-based methodology where the important algorithmic ingredients (features) of HGA are chosen and carefully examined. The following components were investigated: initial population, selection of parents, crossover procedures, number of offspring per generation, local improvement, replacement of population, population restart). The obtained results of the conducted experiments demonstrate how the methodical redesign (reconfiguration) of particular components improves the overall performance of the hybrid genetic algorithm.

scholarly journals Mapping the convergence of genetic algorithms

2006 ◽  
Vol 2006 ◽  
pp. 1-16 ◽  
Author(s):  
Zvi Drezner ◽  
George A. Marcoulides
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Quadratic Assignment ◽  
Valuable Insight ◽  
Cluster Analytic ◽  
Insight Into

This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. The procedure is illustrated with a hybrid genetic algorithm applied to the quadratic assignment problem. Results provide valuable insight into how population members are selected as the number of generations increases and how genetic algorithms approach stagnation after many generations.

scholarly journals A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 108
Author(s):  
Alfonsas Misevičius ◽  
Dovilė Verenė
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Search Algorithm ◽  
Quadratic Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Hierarchical Architecture ◽  
Quadratic Assignment ◽  
Hierarchical Algorithm ◽  
Local Improvement ◽  
Local Optimizer

In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original, hierarchical architecture. In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. The results of the conducted computational experiments demonstrate the promising performance and competitiveness of the proposed algorithm.

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